Assessment of Atherosclerotic Plaque Burden: Comparison of AI-QCT versus SIS, CAC, Visual and CAD-RADS Stenosis Categories

Abstract Purpose: This study assesses the agreement of Atherosclerosis Imaging-Quantitative Computed Tomography (AI-QCT) with qualitative approaches to atherosclerotic disease burden codified in the multisociety 2022 CAD-RADS 2.0 Expert Consensus. Methods: 105 patients who underwent cardiac computed tomography angiography (CCTA) for chest pain were evaluated by a blinded core laboratory through FDA-cleared software (Cleerly, Denver, CO) that performs AI-QCT through artificial intelligence, analyzing factors such as % stenosis, plaque volume, and plaque composition. AI-QCT plaque volume was then staged by recently validated prognostic thresholds, and compared with CAD-RADS 2.0 clinical methods of plaque evaluation (segment involvement score (SIS), coronary artery calcium score (CACS), visual assessment, and CAD-RADS percent (%) stenosis) by expert consensus blinded to the AI-QCT core lab reads. Results: Average age of subjects were 59 ± 11 years; 44% women, with 50% of patients at CAD-RADS 1-2 and 21% at CAD-RADS 3 and above by expert consensus. AI-QCT quantitative plaque burden staging had excellent agreement of 93% (k=0.87 95% CI: 0.79-0.96) with SIS. There was moderate agreement between AI-QCT quantitative plaque volume and categories of visual assessment (64.4%; k=0.488 [0.38-0.60]), CACS (66.3%; k=0.488 [0.36-0.61]). Agreement between AI-QCT plaque volume stage and CAD-RADS % stenosis category was also modest. (73.1%; k=0.55 [0.42-0.68]). Conclusion: AI-QCT graded plaque volume stages and SIS demonstrated overall high agreement while agreement of AI-QCT with visual estimate and CACS was moderate. In addition, AI-QCT graded stenosis CAD-RADS was moderate to substantial. There was discordance at small plaque volumes. With ongoing validation, these results demonstrate a potential for AI-QCT as a rapid, reproducible approach to quantify total plaque burden.